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Record W2259056550 · doi:10.22004/ag.econ.205763

Was Sandmo Right? Experimental Evidence on Attitudes to Price Risk and Uncertainty

2015· preprint· en· W2259056550 on OpenAlexfundno aff
Yu Na Lee, Marc F. Bellemare, David R. Just

Bibliographic record

VenueRePEc: Research Papers in Economics · 2015
Typepreprint
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsnot available
FundersUniversity of GuelphUniversity of GeorgiaUniversity of Minnesota
KeywordsAmbiguityEconomicsCertaintyContradictionPrice levelRisk aversion (psychology)HedgeFactor priceMid pricePrice riskEconometricsAmbiguity aversionDistribution (mathematics)MicroeconomicsExpected utility hypothesisMathematical economicsFinancial economicsMonetary economicsMathematicsComputer scienceFutures contract

Abstract

fetched live from OpenAlex

In his seminal 1971 article, Sandmo showed that when faced with an uncertain output price, a risk-averse firm manager would hedge by producing less than he would have when faced with a certain output price. We take Sandmo’s prediction, among other things, to the lab. We study in turn the effects of price risk (i.e., uncertain prices whose distribution is known) and price ambiguity (i.e., uncertain prices whose distribution is not known, but whose range is known) while controlling for our subjects’ income risk preferences. Our experimental protocol closely mimics Sandmo’s theoretical model. For price risk, we use a two-stage randomization strategy aimed first at studying the effect of price uncertainty relative to price certainty, and then the effect of increases in price uncertainty conditional on there being price uncertainty. For price ambiguity, we use the same randomization strategy to study the effect of price ambiguity relative to price certainty while preventing our subjects from guessing the shape of the price distribution. For price risk, we find that, in stark contradiction to Sandmo’s theoretical result, the presence of price uncertainty causes subjects to produce more than they do under price certainty, but that increases in price uncertainty makes them decrease their production monotonically. For price ambiguity, results are mixed and depend on whether the portion of the experiment aimed at eliciting our subjects’ income risk aversion is played before or after the price uncertainty game. Lastly, we use our price risk data to study the problem structurally, in order to get at preference heterogeneity, and find that our structural results are consistent with our reduced-form results.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.229
GPT teacher head0.484
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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